store = {}
store['args']={'num_inference_samples': 10, 'available_sample_k': 5, 'seed': 107646, 'type': 'AcquisitionFunction.random', 'acquisition_method': 'AcquisitionMethod.independent', 'experiment_description': 'EMNIST_balanced with random acquisition', 'batch_size': 64, 'scoring_batch_size': 512, 'test_batch_size': 512, 'validation_set_size': 16384, 'early_stopping_patience': 3, 'epochs': 40, 'epoch_samples': 20224, 'target_accuracy': 0.85, 'target_num_acquired_samples': 300, 'initial_percentage': 50, 'reduce_percentage': 10, 'min_remaining_percentage': 30, 'min_candidates_per_acquired_item': 100, 'log_interval': 20, 'dataset': 'DatasetEnum.emnist', 'initial_samples': [], 'experiment_task_id': 'emnist_balanced_independent_random_k10_b5_107646', 'experiments_laaos': './experiment_configs/emnist_random/configs.py', 'no_cuda': False, 'quickquick': False, 'initial_samples_per_class': 2}
store['cmdline']=['./src/ignite_mnist.py', '--experiment_task_id=emnist_balanced_independent_random_k10_b5_107646', '--experiments_laaos=./experiment_configs/emnist_random/configs.py']
store['iterations']=[]
store['initial_samples']=[]
store['iterations'].append({'num_epochs': 0, 'test_metrics': {'accuracy': 0.01776595744680851, 'nll': 3.8714848478690103}, 'chosen_samples': [40586, 78646, 54399, 6288, 72546], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 22.270131364000008, 'batch_acquisition_elapsed_time': 0.016757911000013337})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.061436170212765956, 'nll': 47.503226655494664}, 'chosen_samples': [101882, 24138, 103602, 102890, 75209], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 43.46988121499999, 'batch_acquisition_elapsed_time': 0.0029189050000013594})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.0799468085106383, 'nll': 39.630604433607544}, 'chosen_samples': [94594, 6299, 51588, 56333, 17306], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 41.86643888200001, 'batch_acquisition_elapsed_time': 0.0028362709999782965})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.10617021276595745, 'nll': 33.03960139908816}, 'chosen_samples': [40889, 38010, 92542, 15562, 36638], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 60.211356285999955, 'batch_acquisition_elapsed_time': 0.003043280000042614})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.11430851063829787, 'nll': 34.85836372141254}, 'chosen_samples': [105403, 112162, 6756, 98875, 48246], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 42.311152005000054, 'batch_acquisition_elapsed_time': 0.0031003820000705673})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.14595744680851064, 'nll': 34.99990698334131}, 'chosen_samples': [52880, 106828, 15564, 76359, 106961], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 79.48294307100002, 'batch_acquisition_elapsed_time': 0.0028016300000217598})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.14797872340425533, 'nll': 31.23027544978451}, 'chosen_samples': [76862, 50103, 65779, 859, 436], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 51.768091625000125, 'batch_acquisition_elapsed_time': 0.0029739340000105585})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.15521276595744682, 'nll': 27.29072235399865}, 'chosen_samples': [109974, 18196, 86447, 70288, 107200], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 51.474228825999944, 'batch_acquisition_elapsed_time': 0.0029636920000939426})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.17154255319148937, 'nll': 28.676646714101445}, 'chosen_samples': [83282, 33312, 78989, 17579, 75396], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 41.50421036900002, 'batch_acquisition_elapsed_time': 0.0028789200000574056})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.17622340425531915, 'nll': 25.456329873872573}, 'chosen_samples': [48309, 28358, 28289, 24324, 4875], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 42.081921588999876, 'batch_acquisition_elapsed_time': 0.0029179140001360793})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.1896808510638298, 'nll': 23.122398481273272}, 'chosen_samples': [87724, 26352, 93900, 42551, 89959], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 59.97782395200011, 'batch_acquisition_elapsed_time': 0.0032763819999672705})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.2227127659574468, 'nll': 19.438685021261588}, 'chosen_samples': [38204, 94006, 5106, 21180, 62457], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 42.465242709999984, 'batch_acquisition_elapsed_time': 0.002778433999992558})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.24063829787234042, 'nll': 20.09705216287172}, 'chosen_samples': [98068, 65872, 44730, 45852, 98857], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 43.05541140599985, 'batch_acquisition_elapsed_time': 0.0028641210001296713})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.23792553191489363, 'nll': 22.29469553868758}, 'chosen_samples': [83457, 108071, 111052, 46545, 48084], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 42.14963092700009, 'batch_acquisition_elapsed_time': 0.0028274910000618547})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.26218085106382977, 'nll': 19.62855061421115}, 'chosen_samples': [7859, 10432, 81333, 74546, 57034], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 42.78822326400018, 'batch_acquisition_elapsed_time': 0.0030893250000190164})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.25930851063829785, 'nll': 17.609586918690738}, 'chosen_samples': [27483, 1904, 86878, 17196, 70490], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 43.54620595999995, 'batch_acquisition_elapsed_time': 0.002926759000047241})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.26920212765957446, 'nll': 16.77048940803713}, 'chosen_samples': [105342, 22243, 51413, 60046, 50347], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 44.09433113699993, 'batch_acquisition_elapsed_time': 0.0029922130001978076})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.25196808510638297, 'nll': 16.698858552366495}, 'chosen_samples': [90264, 87260, 50455, 23245, 15444], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 44.47008420399993, 'batch_acquisition_elapsed_time': 0.002835591999883036})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.26388297872340427, 'nll': 15.07605745912486}, 'chosen_samples': [105098, 85373, 112161, 73339, 69359], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 43.81813391499986, 'batch_acquisition_elapsed_time': 0.0029048679998595617})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.27627659574468083, 'nll': 13.421213968994772}, 'chosen_samples': [61469, 67426, 9562, 72995, 102189], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 43.17030405299988, 'batch_acquisition_elapsed_time': 0.002834992000089187})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.2579787234042553, 'nll': 14.849604573331614}, 'chosen_samples': [32064, 60657, 27492, 94164, 11103], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 42.77284017900001, 'batch_acquisition_elapsed_time': 0.0028108809999594087})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.2603191489361702, 'nll': 13.544072049860626}, 'chosen_samples': [34721, 12380, 8804, 97870, 68382], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 38.86484825600019, 'batch_acquisition_elapsed_time': 0.002919096000141508})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.29212765957446807, 'nll': 12.42653030092602}, 'chosen_samples': [85988, 37484, 53330, 78191, 71334], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 39.667014737000045, 'batch_acquisition_elapsed_time': 0.002639382000097612})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.28638297872340424, 'nll': 11.789395783956383}, 'chosen_samples': [90981, 27070, 21412, 81051, 95738], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 38.997424263000084, 'batch_acquisition_elapsed_time': 0.0027182480000647047})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.3098936170212766, 'nll': 11.126900662108305}, 'chosen_samples': [76929, 43767, 81099, 53555, 31888], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 44.33405722700013, 'batch_acquisition_elapsed_time': 0.0029253200000312063})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.3046276595744681, 'nll': 10.920160928657081}, 'chosen_samples': [96615, 109373, 2527, 40649, 103153], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 43.40027447600005, 'batch_acquisition_elapsed_time': 0.0031202850000227045})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.3278191489361702, 'nll': 10.12117654805361}, 'chosen_samples': [82167, 319, 57212, 83707, 63222], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 43.29419902299992, 'batch_acquisition_elapsed_time': 0.0031267169999864564})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.3323404255319149, 'nll': 9.002999284644712}, 'chosen_samples': [12037, 49195, 106935, 106394, 65778], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 42.80875567299995, 'batch_acquisition_elapsed_time': 0.002971236999883331})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.33170212765957446, 'nll': 9.207627944536668}, 'chosen_samples': [57586, 36428, 106212, 76732, 107257], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 43.52125584200007, 'batch_acquisition_elapsed_time': 0.0030432849998760503})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.3237765957446809, 'nll': 8.917720653115751}, 'chosen_samples': [20471, 43684, 32527, 39537, 105779], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 43.003572311000426, 'batch_acquisition_elapsed_time': 0.0028045369999745162})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.32941489361702125, 'nll': 9.603923144421362}, 'chosen_samples': [99412, 26177, 72988, 91089, 73490], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 42.886357339999904, 'batch_acquisition_elapsed_time': 0.0029373669999586127})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.3356914893617021, 'nll': 9.029591390765729}, 'chosen_samples': [88478, 57497, 39203, 78031, 36881], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 43.59015820000013, 'batch_acquisition_elapsed_time': 0.003017437000380596})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.35095744680851065, 'nll': 7.577736347727955}, 'chosen_samples': [44705, 85892, 52029, 42198, 43012], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 43.474836614000196, 'batch_acquisition_elapsed_time': 0.003006821999861131})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.3569148936170213, 'nll': 9.148703663696}, 'chosen_samples': [34163, 70571, 27481, 39192, 106320], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 42.729062712000086, 'batch_acquisition_elapsed_time': 0.0028966039999431814})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.35335106382978726, 'nll': 8.939259680558077}, 'chosen_samples': [69781, 75606, 108254, 63967, 98200], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 43.128663242999664, 'batch_acquisition_elapsed_time': 0.003047876999971777})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.359468085106383, 'nll': 7.453526507598288}, 'chosen_samples': [88212, 13413, 77525, 79208, 45033], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 43.26247063200026, 'batch_acquisition_elapsed_time': 0.002930527999978949})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.35723404255319147, 'nll': 7.110715649664718}, 'chosen_samples': [87807, 59611, 14507, 104237, 70578], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 43.321966807999615, 'batch_acquisition_elapsed_time': 0.0030272520002654346})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.36664893617021277, 'nll': 7.27850326427627}, 'chosen_samples': [26591, 62613, 67994, 64613, 21631], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 43.06751638300011, 'batch_acquisition_elapsed_time': 0.0031192350002129388})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.3673936170212766, 'nll': 7.620257420556345}, 'chosen_samples': [112349, 84321, 82015, 59483, 80210], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 43.923317608999696, 'batch_acquisition_elapsed_time': 0.0031008859996290994})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.34547872340425534, 'nll': 7.31518765795453}, 'chosen_samples': [26667, 45217, 74729, 33533, 60000], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 44.493560533000164, 'batch_acquisition_elapsed_time': 0.00303906699991785})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.38941489361702125, 'nll': 6.792040445498014}, 'chosen_samples': [6858, 48736, 61078, 110299, 93141], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 42.367159700999764, 'batch_acquisition_elapsed_time': 0.0030067349998716963})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.4017553191489362, 'nll': 6.689202880753165}, 'chosen_samples': [84098, 67307, 50277, 106646, 109533], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 43.294668365000234, 'batch_acquisition_elapsed_time': 0.00297531300020637})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.41, 'nll': 6.463778571932558}, 'chosen_samples': [100172, 16361, 71355, 11188, 35051], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 42.88717804199996, 'batch_acquisition_elapsed_time': 0.0029954800002087723})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.38409574468085106, 'nll': 6.7848821680612375}, 'chosen_samples': [91094, 46101, 47100, 9753, 22282], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 43.77938418799977, 'batch_acquisition_elapsed_time': 0.002736888000072213})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.4027659574468085, 'nll': 6.018310692513559}, 'chosen_samples': [9794, 92294, 72934, 69694, 3309], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 43.35552580900003, 'batch_acquisition_elapsed_time': 0.0029313169998204103})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.42021276595744683, 'nll': 6.131338488910425}, 'chosen_samples': [108117, 38922, 55379, 5213, 77983], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 43.44989089599994, 'batch_acquisition_elapsed_time': 0.003142635000131122})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.4076063829787234, 'nll': 6.427541696522146}, 'chosen_samples': [82556, 25978, 16372, 85087, 94364], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 43.77871237100044, 'batch_acquisition_elapsed_time': 0.0029210179995970975})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.4153191489361702, 'nll': 5.813069435373266}, 'chosen_samples': [4622, 98575, 1141, 45007, 18643], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 43.40865235299998, 'batch_acquisition_elapsed_time': 0.003023843999926612})
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